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Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving.
Probability and statistics: The science of uncertainty
Evans, M. J. and Rosenthal, J. S · 2004
Earlier work this paper cites.
Grinstead and Snell’s introduction to probability
Grinstead, C. M. and Snell, J. L · 2006
Earlier work this paper cites.
CORRAL’S VECTOR CALCULUS
Corral, M · 2008
Earlier work this paper cites.
Calculus
Guichard, D · 2009
Earlier work this paper cites.
Beginning and intermediate algebra
Wallace, T · 2010
Earlier work this paper cites.
Precalculus
Stitz, C. and Zeager, J · 2013
Earlier work this paper cites.
How humans learn to think mathematically: Exploring the three worlds of mathematics
Tall, D · 2013
Earlier work this paper cites.
Elementary Differential Equations
Trench, W. F · 2013
Earlier work this paper cites.
Sequence-level knowledge distillation
Kim, Y. and Rush, A. M · 2016
Earlier work this paper cites.
A First Course in Linear Algebra, 2017A version (Lyryx)
Kuttler, K. and Farah, I · 2017
Earlier work this paper cites.
Deep neural solver for math word problems
Wang, Y., Liu, X., and Shi, S · 2017
Earlier work this paper cites.
Matrix theory and linear algebra, 2018
Selinger, P · 2018
Cited alongside, same era.
Ape210k: A large-scale and template-rich dataset of math word problems
Zhao, W., Shang, M., Liu, Y., Wang, L., and Liu, J · 2020
Cited alongside, same era.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al · 2021
Cited alongside, same era.
Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Luo, H., Sun, Q., Xu, C., Zhao, P., Lou, J., Tao, C., Geng, X., Lin, Q., Chen, S., and Zhang, D · 2023
Later among the works it cites.
Tal-scq5k, 2023
TAL · 2023
Later among the works it cites.
Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
Later among the works it cites.
How far can camels go? exploring the state of instruction tuning on open resources
Wang, Y., Ivison, H., Dasigi, P., Hessel, J., Khot, T., Chandu, K. R., Wadden, D., MacMillan, K., Smith, N. A., Beltagy, I., et al · 2023
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Cited alongside, same era.
Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Cited alongside, same era.
Generative ai for math: Abel
Chern, E., Zou, H., Li, X., Hu, J., Feng, K., Li, J., and Liu, P · 2023
Cited alongside, same era.
PAL: Program-aided language models
Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., and Neubig, G · 2023
Cited alongside, same era.
Tora: A tool-integrated reasoning agent for mathematical problem solving
Gou, Z., Shao, Z., Gong, Y., Yang, Y., Huang, M., Duan, N., Chen, W., et al · 2023
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Cited alongside, same era.
Later among the works it cites.
Wizardlm: Empowering large language models to follow complex instructions
Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., and Jiang, D · 2023
Later among the works it cites.
Metamath: Bootstrap your own mathematical questions for large language models
Yu, L., Jiang, W., Shi, H., Yu, J., Liu, Z., Zhang, Y., Kwok, J. T., Li, Z., Weller, A., and Liu, W · 2023
Later among the works it cites.
Mammoth: Building math generalist models through hybrid instruction tuning
Yue, X., Qu, X., Zhang, G., Fu, Y., Huang, W., Sun, H., Su, Y., and Chen, W · 2023
Later among the works it cites.
Evaluating the performance of large language models on gaokao benchmark
Zhang, X., Li, C., Zong, Y., Ying, Z., He, L., and Qiu, X · 2023
Later among the works it cites.
Agieval: A human-centric benchmark for evaluating foundation models
Zhong, W., Cui, R., Guo, Y., Liang, Y., Lu, S., Wang, Y., Saied, A., Chen, W., and Duan, N · 2023
Later among the works it cites.